Method and system for providing recommendations concerning a configuration process

ABSTRACT

A computer-implemented method for providing recommendations, REC, concerning a configuration process is provided to configure an industrial system, SYS, the method including the steps of calculating by a trained graph neural network, GNN, scores, s, for components, c, of a set, C, of configurable component types, ct; generating recommendations, REC, for introducing at least one additional component, c, into the industrial system, SYS, on the basis of the calculated scores, s; and outputting the generated recommendations, REC, to a user by a user interface or executing the generated recommendations.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to PCT Application No.PCT/EP2021/074246, having a filing date of Sep. 2, 2021, which claimspriority to EP Application No. 20197648.7, having a filing date of Sep.23, 2020, the entire contents all of which are hereby incorporated byreference.

FIELD OF TECHNOLOGY

The following relates to a computer-implemented method andrecommendation engine for providing recommendations concerning aconfiguration process to configure an industrial system.

BACKGROUND

An industrial system can comprise a plurality of different kinds ofcomponents, in particular automation components. These automationcomponents can comprise hardware and software components.

The process of configuring an industrial system in engineering projectsinvolves several major steps. The appropriate components have to beselected by a user such that their interplay fulfills all functionalrequirements arising from the intended use case. To do this, an engineerin charge of configuring the industrial system typically utilizesconfiguration software containing a catalog of available automationcomponents. A totally integrated automation portal can provide access toa wide range of digitalized automation services ranging from digitalplanning and integrated engineering to a transparent operation. Eachautomation component can comprise a set of technical features orattributes that have an impact on their capabilities and theircompatibility with other automation components. The technical featurescan comprise both static technical features and configurable technicalattributes. For an engineering of the industrial system, the selectedcomponents have to be coupled so that the connectivity pattern allowsthe intended real-world application. As a consequence, it is notpossible to represent an engineering solution for an industrial systemin the form of a flat list but involve an inherent topology. Finally, ina conventional configuring process, the values of the configurabletechnical attributes of the selected automation components are chosen bythe user such that the components are compatible and the whole subsystemcan operate in the intended scenarios.

Conventional systems may implement predefined recommendation rules suchas “if A then B”. Further, an implicit recommendation of a nextcomponent to be added to the industrial system may be provided bysorting of a corresponding list of possible options, i.e., all availablecomponents and/or all available components belonging to a certaincategory. This can be done either based on some manually definedartificial criteria or by employing one of the collaborativefiltering-based techniques. Collaborative filtering-based techniques canbe augmented by additional contextual information or information about asequence in which items or components are added to the already existingpartially configured industrial system. However, none of theconventional recommendation systems are capable of providing informationhow to connect the configured components with each other.

SUMMARY

An aspect relates to a method and an apparatus for increasing theefficiency of a configuration process used to configure an industrialsystem.

Embodiments of the invention provide according to the first aspect acomputer-implemented method for providing recommendations concerning aconfiguration process to configure an industrial system wherein themethod comprises the steps of:

-   -   calculating by a trained graph neural network scores for        components of a set of configurable component types,    -   generating recommendations for introducing at least one        additional component into the industrial system on the basis of        the calculated scores,    -   outputting the generated recommendations to a user by a user        interface or executing the generated recommendations.

Further on, the graph neural network is trained to encode componentfeatures and a topology of the industrial system configured by theconfiguration process. The component features can comprise both staticfeatures and configurable attributes of the respective component.

In a further possible embodiment of the computer-implemented methodaccording to the first aspect of the present invention, the topology ofthe industrial system is represented by a topology graph stored in amemory,

-   -   wherein a vertex set of the topology graph contains vertices        representing configured components of the industrial system and    -   wherein an edge set of the topology graph contains edges between        two vertices representing connections between two corresponding        components within the industrial system configured by the        configuration process.

In a further possible embodiment of the computer-implemented methodaccording to the first aspect of the present invention, each vertexwithin the vertex set of the topology graph of the industrial systemrepresenting a corresponding component in the industrial systemcomprises an associated feature vector specifying technical attributesof the respective component.

In a further possible embodiment of the computer-implemented methodaccording to the first aspect of the present invention, each featurevector of a vertex within the vertex set of the stored topology graph ofthe industrial system comprises a one-hot encoding of a component typeof the respective component.

In a further possible embodiment of the computer-implemented methodaccording to the first aspect of the present invention, the featurevectors of all vertices of the vertex set of the stored topology graphof the industrial system form a feature matrix stored in the memory.

In a still further possible embodiment of the computer-implementedmethod according to the first aspect of the present invention, anembedding for each configured component of the industrial system isperformed by processing the feature vector of the corresponding vertexwithin the vertex set of the stored topology graph of the industrialsystem along with the feature vectors of all neighboring vertices in thestored topology graph by the trained graph neural network to generate acontext-aware embedding with an embedding size of the respectivecomponent.

In a further possible embodiment of the computer-implemented methodaccording to the first aspect of the present invention, the embedding isperformed for all configured components of the industrial system togenerate a first embedding matrix, H∈

^(n×d), wherein n is the number of vertices v in the vertex set V of thestored topology graph G and d is the embedding size.

In a further possible embodiment of the computer-implemented methodaccording to the first aspect of the present invention, embedding isperformed for all component types of components to generate a secondembedding matrix Z with Z∈

^(m×d),

-   -   wherein m is the number of configurable component types and d is        the embedding size.

In a further possible embodiment of the computer-implemented methodaccording to the first aspect of the present invention, a matrixmultiplication of the second embedding matrix Z with the transposedfirst embedding matrix H^(T) is performed to calculate a score matrix Swith S∈

^(m×n), wherein each entry su of the calculated score matrix S containsa score s which indicates a plausibility of selecting a component c ofthe component type ct_(i) and connecting it to an already configuredcomponent c of the component type ct_(j).

In a further possible embodiment of the computer-implemented methodaccording to the first aspect of the present invention, recommendationsfor introducing at least one additional component c into the industrialsystem are generated for the component types having the highest scores sin the calculated score matrix S.

In a further possible embodiment of the computer-implemented methodaccording to the first aspect of the present invention, in response toan introduction of an additional component c into the industrial systemby a user via the user interface, the stored topology graph of theindustrial system is automatically extended with an additional vertexcorresponding to the introduced component c and extended with an edgebetween the additional vertex v and the vertex of the at least onecomponent c to which the additional component c has been connected to.

In a further possible embodiment of the computer-implemented methodaccording to the first aspect of the present invention, the featurevector x_(v) of the vertex v corresponding to the additional component cis initialized with a one-hot encoding of the component type ct of theadditional component c and the remaining entries of the feature vectorx_(v) are set to zero.

In a further possible embodiment of the computer-implemented methodaccording to the first aspect of the present invention, the initializedfeature vector x_(v) of the vertex v corresponding to the additionalcomponent c is passed through the trained graph neural network GNN toproduce for the respective added component c an embedding vectorh_(n+1), which is fed into a prediction model g to generate a predictionvector {circumflex over (x)}_(n+1) having entries output to the user viathe user interface as recommendations for the technical attributes ofthe respective added component c.

Embodiments of the invention provide according to a further aspect arecommendation engine for providing recommendations concerning aconfiguration process to configure an industrial system,

-   -   wherein the recommendation engine is adapted to calculate by a        trained graph neural network GNN scores s for components c of a        set C of configurable component types ct and to generate        recommendations for introducing at least one additional        component c into the industrial system on the basis of the        calculated scores s,    -   wherein the generated recommendations are output to a user by a        user interface or executed automatically, and wherein the graph        neural network, GNN, is trained to encode component features and        a topology of the industrial system configured by the        configuration process, wherein the component features comprise        static features and configurable attributes a of the respective        component, c.

BRIEF DESCRIPTION

FIG. 1 shows a flowchart of a possible exemplary embodiment of acomputer-implemented method according to the first aspect of the presentinvention;

FIG. 2 shows a diagram for illustrating a possible exemplary embodimentof a recommendation engine according to a further aspect of the presentinvention;

FIG. 3 shows further flowcharts for illustrating a computer-implementedmethod according to the first aspect of embodiments of the presentinvention.

DETAILED DESCRIPTION

As can be seen from the flowchart in FIG. 1 , the computer-implementedmethod for providing recommendations concerning a configuration processto configure an industrial system SYS can comprise in a possibleembodiment several main steps.

The computer-implemented method according to embodiments of the presentinvention can be used to provide assistance in the process ofconfiguring engineering projects concerning an industrial system SYS.The computer-implemented method can provide recommendations concerningthe configuration process of the industrial system using a trained graphneural network GNN. Graph neural networks GNNs are connectionist modelsthat capture the dependence of graphs via a message passing betweennodes of graphs. Unlike a standard neural network, graph neural networkscan retain a state that can represent information from its neighborhoodwith arbitrary depths.

In the computer-implemented method according to the first aspect ofembodiments of the present invention as illustrated by the flowchart ofFIG. 1 , in a first step S1, scores s for components c of a set C ofconfigurable component types are calculated by a trained graph neuralnetwork GNN.

In a further step S2, recommendations for introducing at least oneadditional component c into the industrial system SYS are generated onthe basis of the calculated scores s.

Finally, in a third step S3, the generated recommendations are eitheroutput to a user by a user interface or automatically executed.

The graph neural network GNN used to calculate the scores s in step S1has been trained to encode component features and a topology of theindustrial system SYS configured during the configuration process. Thecomponent features can comprise static features and configurabletechnical attributes of the respective component c. The static featuresor attributes of an automation component c are invariable and do notchange over time. These static features may for instance comprise asize, height or volume of the respective automation component c. Anotherexample of static features may also comprise for instance the number ofports provided by the respective automation component. A further examplefor a static feature of an automation component c is the applied linevoltage or supply voltage for the respective automation component c.Besides the static features, the component c can comprise configurabletechnical attributes such as a temperature range where the automationcomponent c can be used in the industrial system SYS or whether therespective automation component c has to be fail-safe or not.

The topology of the respective industrial system SYS to be configuredduring the configuration process can be represented by a topology graphG=(V, E) and can be stored in a memory of a recommendation engine. Thevertex set V of the topology graph G contains vertices v representingconfigured components c of the industrial system SYS. Further, an edgeset E of the topology graph G contains edges between two vertices v_(i),v_(j) representing connections between two corresponding componentsc_(i), c_(j) within the industrial system SYS to be expanded during theconfiguration process.

Each vertex v within the vertex set V of the topology graph G of theindustrial system SYS representing a corresponding component c in theindustrial system SYS comprises an associated feature vector x_(v). Afeature vector x_(v) of a vertex v specifies technical attributes of therespective automation component c. These technical attributes cancomprise both static features and also configurable technical attributesof the respective automation component c. Each feature vector x_(v) of avertex v within the vertex set V of the stored topology graph G of theindustrial system SYS can comprise a one-hot encoding of a componenttype ct of the respective automation component c. In a possibleembodiment, the feature vectors x_(v) of all vertices v of the vertexset V of the stored topology graph G of the industrial system SYS form afeature matrix X stored in the memory of the recommendation engine.

An embedding for each configured component c of the industrial systemSYS is performed by processing the feature vector x_(v) of thecorresponding vertex v within the vertex set V of the stored topologygraph G of the industrial system SYS along with the feature vectorsx_(v) of all neighboring vertices v in the stored topology graph G bythe trained graph neural network GNN to generate a context-awareembedding h_(v) with an embedding size d of the respective component c.The neighboring vertices v can comprise directly neighboring vertices vwithin the graph but also vertices v connected indirectly via severalhops in the topology graph G. The graph neural network GNN is trained toencode both the component features and the topology of the respectiveengineering project. A partial, i.e., not completed engineering project,is represented by the topology graph G=(V, E). Each component c has anassociated feature vector x_(v) that specifies the configured technicalattributes.

The embedding is performed for all configured components c in theindustrial system SYS to generate a first embedding matrix H∈

^(n×d), wherein n is the number of vertices v in the vertex set V of thestored topology graph G and d is the embedding size.

The embedding is performed for all component types ct of components c togenerate a second embedding matrix Z with Z∈

^(m×d), wherein m is the number of configurable component types ct and dis the embedding size. To allow efficient computation on the node level,a so-called embedding is produced for every configured component crepresented by a corresponding vertex v in the vertex set V. For thispurpose, the trained graphical neural network GNN is employed. Thetrained graphical neural network GNN takes as an input the featurevector x_(v) of a given center node or vertex v within the graph alongwith all the feature vectors x_(v) of its neighboring vertices v toproduce a context-ware embedding h_(v). Heuristically speaking, aforward pass through the graphical neural network GNN first aggregatesthe feature vectors x_(v) of all the automation components c which areconnected with the vertex v of the center node. Then, in a second step,the graphical neural network GNN combines this neighborhood informationwith the feature vector x_(v) to produce an embedding h_(v). It ispossible to stack multiple layers of the graphical neural network GNN toobtain a more expressive encoder. The computations can be redone orreiterated for every node or vertex v to form the first embedding matrixH.

Since it is a goal to produce a score s for all component types c of thecomponent type set C, d-dimensional embeddings for all component typesct are generated. This can be achieved by reusing components of thegraphical neural network GNN or via an embedding look-up. In most cases,the resulting embedding matrix is denoted with Z∈

^(m×d), wherein m is the number of configurable component types ct and dis the embedding size.

In a further step, a matrix multiplication of the second embeddingmatrix Z with the transposed first embedding matrix H^(T) is performedto calculate a score matrix S with S∈

^(m×n), wherein each entry s_(ij) of the calculated score matrix Scontains a score s which indicates a plausibility or suitability ofselecting a component c of the component type ct_(i) and connecting itto an already configured component c of the component type ct_(j).Accordingly, a score s is produced for every item when performing amatrix multiplication S=Z·H^(T) corresponding to a linear decoding step.Hence, if properly calibrated, an entry S_(ij) of S∈

^(m×n) contains scores s that indicate the plausibility of selecting acomponent type ct_(i) and connecting it to the already configuredcomponent c of the component type ct_(j). On that basis, it is possibleto generate recommendations to a user to add components c to the alreadypartially configured industrial system SYS that come with the highestscores s.

The user may now proceed in different ways. The user may add therecommended automation component c to the already existing partiallyconfigured industrial system SYS or may also add another not recommendedcomponent c to the industrial system SYS. In a further option, the usermay decide not to add any further component c and finalize theengineering project.

In case that the user adds the recommended component c or another notrecommended component, this corresponds to adding a new vertex v_(n+a)to the topology graph G and connecting it to an existing node or vertexv_(j). In this case, the graph G is extended with the new vertex andedge and the process is reiterated. Otherwise, if the user does not addany further component the configuration process can be terminated.

In response to an introduction of an additional component c into theindustrial system by a user via the user interface, the stored topologygraph G of the industrial system SYS can be automatically extended withthe additional vertex v corresponding to the introduced component c andextended with an edge between the additional vertex v and the vertex ofthe component c to which the additional component c has been connectedto. The feature vector x_(v) of the vertex v corresponding to theadditional component c can be initialized with a one-hot encoding of thecomponent type ct of the additional component c and the remainingentries of the feature vector x_(v) are set to zero. In a possibleembodiment, the initialized feature vector x_(v) of the vertex vcorresponding to the additional component c can be passed through thetrained graph neural network GNN to produce for the respective addedcomponent c an embedding h_(n+1) which can be fed into a predictionmodel g to generate a prediction vector {circumflex over (x)}_(n+1)having entries output to the user via the user interface asrecommendations for the technical attributes of the respective addedcomponent c.

FIG. 2 shows a diagram for illustrating a possible embodiment of arecommendation engine 1 according to a further aspect of the embodimentsof the present invention. The recommendation engine 1 illustrated inFIG. 2 comprises in the illustrated embodiment three main modules. Therecommendation engine 1 receives via an input interface an inputpartially configured engineering project represented by the topologygraph G having vertices v connected by edges. Each vertex v or node ofthe graph G represents a configured component c of the respectiveindustrial system SYS. These automation components c can comprise bothhardware and software components. These automation components c can forinstance comprise programmable logic controllers PLC, human-machineinterfaces HMI, motion controllers, server amplifiers, variable speeddrivers or robotic components. Depending on the use case, there can be awide variety of different hardware or software components that can beconfigured and used during a configuration process of an industrialsystem SYS. In the illustrated example of FIG. 2 , the graph G of thepartially configured engineering process comprises nine vertices v₁ tov₉ each representing a corresponding component c of a specific componenttype ct. As illustrated in FIG. 2 , the vertices v₁, v₂, v₃, v₇represent components c of a first component type ct. Also, the verticesv₄, v₉ are of the same component type ct. Further, the components crepresented by the vertices v₅, v₈ are of the same component type ct.The vertex v₆ represents a component c of a further component type ct.The vertices v are connected via edges of an edge set E representingconnections between two corresponding components within the industrialsystem SYS.

The recommendation engine 1 can be used to provide recommendationsconcerning a configuration process to configure and to expand therespective industrial system SYS. The recommendation engine 1 as shownin FIG. 2 is adapted to calculate by a trained graph neural network GNNscores s for components c of a set C of configurable component types ctand to generate automatically recommendations introducing at least oneadditional component c into the industrial system SYS on the basis ofthe calculated scores s. The generated recommendations can be eitheroutput to a user by a user interface or executed automatically by anexecution engine of the system.

In the illustrated example, the recommendation engine 1 recommendsadding an additional component c represented by the vertex v₁₀ of thegraph G′ into the partially configured industrial system SYS representedby the graph G supplied to the recommendation engine 1. v₁₀ represents acomponent c which is connected to the components c represented by thevertices v₆, v₈, v₉. The recommendation engine 1 according toembodiments of the present invention does not only give a recommendationwhat kind of component c has to be added to the partially configuredindustrial system SYS but also to which other components c it shall beconnected to. Further, the recommendation engine 1 does also provideinformation about the configurable attributes of the added automationcomponent c. In the example illustrated in FIG. 2 , the configurableattributes of the added component c comprise as a maximum temperature ofthe added component c a temperature value of 60. Further, it indicatesthat the added component c shall be fail-safe. In the illustratedembodiment of FIG. 2 , the recommendation engine 1 comprises three mainmodules including an encoder module 2, a link prediction module 3 and anattribute prediction module 4.

The encoder module 2 comprises a trained graph neural network GNN whichcan calculate scores s for components c of a set C of configurablecomponent types ct. Based on the calculated scores s, recommendationsfor introducing at least one additional component into the industrialsystem are generated. The link prediction module 3 is used to predictlinks or edges indicating to which components c the added component cshall be connected to. The attribute prediction module 4 can use aprediction model g to generate a prediction vector having entries outputto the user via a user interface as recommendations for the technicalattributes of the respective added component c such as fail-safe=trueand x maximum operation temperature=60.

FIG. 3 shows a further diagram for illustrating the computer-implementedmethod according to embodiments of the present invention. As illustratedin FIG. 3 , the partially configured engineering project represented bygraph G is supplied to a trained graph neural network GNN performing anencoding ENC. The linear decoder DEC performs a matrix multiplication ofthe second embedding matrix Z with the transposed first embedding matrixH^(T) to calculate a score matrix S wherein each entry Si_(j) of thecalculated score matrix S contains a score s which indicate aplausibility of selecting a component c of the component type ct_(i) andconnecting it to an already configured component c of the component typect_(j). Then, in the illustrated example, a user may perform a componentselection SFL to expand the already existing partial engineering projectwherein the expanded engineering project or industrial system SYS can beillustrated by a graph G′ as shown also in the example of FIG. 2 . Theexpanded engineering project or industrial system SYS illustrated by theexpanded graph G′ can then be processed again by the trained graphneural network GNN to provide an encoding ENCI of the system includingthe new added component c. This can be supplied to a prediction model gto predict configurable technical attributes a or features of therespective component c.

The graph neural network GNN can be trained from historical engineeringprojects. The training data T can consist of historical engineeringprojects that were configured in the past. That means that T={(G₁, X₁),(G₂, X₂), . . . , (G_(T), X_(T))} wherein each G corresponds to anetwork representation of an engineering project and X_(i) to a featurematrix. Heuristically speaking, one first deletes parts of structures in(G_(i), X_(i)) and then aims to recover these structures by therecommendation system according to embodiments of the present invention.Thereby, it is possible to obtain an approximation of partiallyconfigured engineering projects. Then, the system SYS is trained andvalidated by its ability to complete the projects as they werepreviously configured before the deletion step. More concretely, it ispossible to train the parameters of the graph neural network GNN torecommend previously deleted items in the vertex set V leading to a lossL_(R). Moreover, it is possible to train the graph neural network GNNand the prediction model g with regard to the abilities to restore thefeature matrix X, leading to a prediction loss L_(P). In a possibleembodiment, a joint loss L=L_(R) L_(P) can be formed such that the wholemodel can be trained end-to-end.

With the computer-implemented method according to embodiments of thepresent invention, it is possible to exploit historical examples ofindustrial engineering projects to not only provide details on where toconnect a selected component c but also to predict values of technicalattributes of the respective component c. The computer-implementedmethod and engine is more scalable than conventional systems relying onmanually defined rules. In addition, depending on the complexity of theunderlying technical systems, the computer-implemented method accordingto embodiments of the present invention allows to discover more complexor obscure patterns to base recommendations on than those that a domainexpert in charge of maintaining the collection of rules could easilyspecify. Compared to existing data-driven methods, thecomputer-implemented method according to embodiments of the presentinvention is capable of providing details on how the automationcomponents c shall be connected with each other as well as the values ofconfigurable technical attributes of the respective automationcomponents. This is possible by using a trained graph neural network GNNand a prediction model g. The computer-implemented method according toembodiments of the present invention can be integrated in aconfiguration software tool to increase the efficiency of aconfiguration process of a complex industrial system SYS. Thecomputer-implemented method can be performed by a recommendationassistance system. In a possible embodiment, the recommendation engine 1can operate in real time while a user is in the process of configuringan engineering project or industrial system SYS. Depending on theparticularities of the use case, quality and amount of training data,the proposed computer-implemented method can be used either to assistthe user in the process of configuring an industrial engineering systemSYS or to complete automatically the configuration procedure. Thecomputer-implemented method according to embodiments of the presentinvention does not only provide for a component selection and topologyconstruction, i.e., how the automation components c are connected, butalso provides for a selection of values of configurable technicalattributes of an added component c such as whether the added component cshall be fail-safe or a value concerning a maximal admissible operationtemperature for the respective added component c.

Although the present invention has been disclosed in the form ofembodiments and variations thereon, it will be understood that numerousadditional modifications and variations could be made thereto withoutdeparting from the scope of the invention.

For the sake of clarity, it is to be understood that the use of “a” or“an” throughout this application does not exclude a plurality, and“comprising” does not exclude other steps or elements.

1. A computer-implemented method for providing recommendations RECconcerning a configuration process to configure an industrial systemSYS, the method comprising: calculating by a trained graph neuralnetwork GNN, scores s for components c of a set C of configurablecomponent types ct; generating recommendations REC for introducing atleast one additional component c into the industrial system SYS on thebasis of the calculated scores s; outputting the generatedrecommendations REC to a user by a user interface or executing thegenerated recommendations; and wherein the graph neural network GNN istrained to encode component features and a topology of the industrialsystem SYS configured by the configuration process, wherein thecomponent features comprise static features and configurable attributesa of the respective component c.
 2. The computer-implemented methodaccording to claim 1, wherein the topology of the industrial system SYSis represented by a topology graph G=(V, E) stored in a memory, whereina vertex set V of the topology graph G contains vertices v representingconfigured components c of the industrial system SYS and wherein an edgeset E of the topology graph G contains edges between two vertices v_(i),v_(j) representing connections between two corresponding componentsc_(i), c_(j) within the industrial system SYS configured by theconfiguration process.
 3. The computer-implemented method according toclaim 2, wherein each vertex v within the vertex set V of the topologygraph G of the industrial system SYS representing a correspondingcomponent c in the industrial system SYS, comprises an associatedfeature vector x_(v) specifying technical attributes of the respectivecomponent c.
 4. The computer-implemented method according to claim 3,wherein each feature vector x_(v) of a vertex v within the vertex set Vof the stored topology graph G of the industrial system SYS comprises aone-hot encoding of a component type ct of the respective component c.5. The computer-implemented method according to claim 1, wherein thefeature vectors x_(v) of all vertices v of the vertex set V of thestored topology graph G of the industrial system SYS form a featurematrix X stored in the memory.
 6. The computer-implemented methodaccording to claim 1, wherein an embedding for each configured componentc of the industrial system SYS is performed by processing the featurevector x_(v) of the corresponding vertex v within the vertex set V ofthe stored topology graph G of the industrial system SYS along with thefeature vectors x_(v) of all neighboring vertices v in the storedtopology graph G, by the trained graph neural network GNN to generate acontext-aware embedding h_(v) with an embedding size d of the respectivecomponent c.
 7. The computer-implemented method according to claim 1,wherein the embedding h_(v) is performed for all configured components cof the industrial system SYS to generate a first embedding matrix H∈

^(n×d), wherein n is the number of vertices v, in the vertex set V ofthe stored topology graph G and d is the embedding size.
 8. Thecomputer-implemented method according to claim 1, wherein embedding isperformed for all component types ct of components c to generate asecond embedding matrix Z, with Z∈

^(m×d), wherein m is the number of configurable component types ct and dis the embedding size.
 9. The computer-implemented method according toclaim 7, wherein a matrix multiplication of the second embedding matrixZ with the transposed first embedding matrix H^(T) is performed tocalculate a score matrix S with S∈

^(m×n), wherein each entry s_(ij) of the calculated score matrix Scontains a score s which indicates a plausibility of selecting acomponent c of the component type ct_(i) and connecting it to an alreadyconfigured component c of the component type ct_(j).
 10. Thecomputer-implemented method according to claim 1, whereinrecommendations for introducing at least one additional component c intothe industrial system SYS are generated for the component types cthaving the highest scores s in the calculated score matrix S.
 11. Thecomputer-implemented method according to claim 1, wherein in response toan introduction of an additional component, c, into the industrialsystem SYS by a user via the user interface, the stored topology graph Gof the industrial system SYS is automatically extended with anadditional vertex v corresponding to the introduced component c andextended with an edge between the additional vertex v and the vertex ofthe at least one component c to which the additional component c hasbeen connected to.
 12. The computer-implemented method according toclaim 1, wherein the feature vector x_(v) of the vertex v correspondingto the additional component c is initialized with a one-hot encoding ofthe component type ct of the additional component c and the remainingentries of the feature vector x_(v) are set to zero.
 13. Thecomputer-implemented method according to claim 12, wherein theinitialized feature vector x_(v) of the vertex v corresponding to theadditional component c is passed through the trained graph neuralnetwork GNN to produce for the respective added component c an embeddingh_(n+1), which is fed into a prediction model g to generate a predictionvector {circumflex over (x)}_(n+1) having entries output to the user viathe user interface as recommendations for the technical attributes ofthe respective added component c.
 14. A recommendation engine forproviding recommendations REC concerning a configuration process toconfigure an industrial system SYS, wherein the recommendation engine isconfigured to calculate scores s by a trained graph neural network GNNfor components c of a set C of configurable component types ct and togenerate automatically recommendations REC for introducing at least oneadditional component c into the industrial system SYS on the basis ofthe calculated scores s, wherein the generated recommendations REC areoutput to a user by a user interface or executed automatically by therecommendation engine, and wherein the graph neural network GNN istrained to encode component features and a topology of the industrialsystem SYS configured by the configuration process, wherein thecomponent features comprise static features and configurable attributesa of the respective component c.